Cancer is one of the most prevalent health issues around the globe. Medical imaging and image processing techniques are an integral part of cancer diagnostic process. From ultrasounds to CT scans to CT MRIs, radiologists need and use the assistance of medical images to diagnose and treat diseases. While conventional therapies such as radiotherapy and chemotherapy will remain a mainstay in treatments, clinicians should look for techniques that allow early, accurate diagnosis of cancer.
Advances in such areas allow earlier detection of abnormalities and risks.
This would be critical in increasing survival and cost effectiveness of treatments. More importantly, it could help decrease mortality rate.
Medical images include data on anatomical structures that are important for accurate and early identification, and in selecting and analyzing suitable treatment. The capabilities of medical imaging have increased vastly over the past years due to essential research and technological evolution.
Such advancement is vital in providing effective diagnoses and bettering patient care. These advancements combined with the power of IT and digital growth help promote greater procedural efficiency in the provision and execution of patient care.
From using big data in medical imaging to the possibilities of 3D imaging, there are few major advancements that showcase the future of medical imaging technology.
The enclosed space in the core of traditional MRI machines tend to cause cooperation issues from patients with claustrophobia when undergoing the scan. The presence of loud noises from the machine could further add to the discomfort and anxiety of the patient.
The machine’s small sized bore serves as another limitation, due to which, obese patients will not be allowed to undergo the scan. Today, there are open MRI machines, that are less restrictive and more open on the sides. Modern scanners can also accommodate obese/heavier individuals without difficulty.
Traditionally, MRI machines are large and stationary due to the size of the superconducting magnet. However, researchers at Japan’s Railway Technical Research Institute have developed a superconducting magnet system that is the size of a palm. They are compact new magnets which are nonetheless powerful and also allow medical imaging using MRIs to be portable. These would be significant milestones in medical imaging, allowing more efficient diagnosis, while also greatly reducing the stress level in patients.
According to Siemens, 3D imaging technologies can detect breast cancer and tumors significantly earlier and more efficiently in comparison to traditional 2D mammography methods. It believes that 3D imaging techniques are beginning to revolutionize existing methods of mammography. The emerging mammography method is tomosynthesis, which essentially enables medical officers and researchers to detect cancers that are overlapped by tissues. Undetected tumour cells through conventional 2D mammography, can be detected through Tomosynthesis – based screening.
In August 2018, technology company Nvidia revealed the Nvidia Clara platform, which is a combination of software and hardware working together to bring AI to future medical instruments. Their Clara software development kit, allow developers to apply a variety of AI-powered applications to existing medical imaging equipment.
A start-up Aidoc revealed what it referred to as the world’s first comprehensive full body technology which uses AI to analyse computed tomography CT scans.
Aidoc’s AI-powered medical imaging technology is able to highlight any abnormal findings on a patient’s medical scan for it to be reviewed by radiologists. The AI solution aims to offer support to radiologists focusing on areas such as the chest, abdomen, spine and head.
Subtle Medical is a member of the Nvidia Inception virtual accelerator program that aims to create MRI machines that acquire images in a quarter of the time while requiring just 1/10 of the contrast dosages to patients.
Subtle Medical founder Enhao Gong said: “We are using AI to improve workflow for MRI and PET exams. Nvidia’s Clara platform will enable us to seamlessly scale our technology to reduce risks from contrast and radiation, taking imaging efficiency and safety to the next level.”
Image processing techniques are another widely used modality in several medical areas for improving and for keeping up with treatment processes, in which time is very crucial to discover the disease in the patient as fast as possible.
The techniques available to detect cancerous cells are as image enhancement, segmentation, feature extraction etc. The accuracy of image segmentation is foremost important, as it deals with human lives and it is highly important for the image to be improved before an examination.
Segmentation algorithms rely on the exactitude and convergence of time. In recent times, there is a compelling necessity to explore and implement new evolutionary algorithms which is crucial to curtail a radiologist’s interpretation of the scans.
GE Healthcare introduced a new medical imaging technology, the LOGIQ E10 radiology ultrasound system. The technology is aided by AI in quickly capturing data and reconstruct images. The digital system employs closed connectivity and advanced algorithms to ensure 48 times more data throughput and ten times the processing power of previous systems.
The cloud connectivity feature of the system is called Tricefy and enables clinicians to share a variety of important medical information with their colleagues and patients such as images, diagnostic information and health records.
According to the team, Convolutional Analysis Operator Learning (CAOL) may be a methodology to learn kernels/features from large datasets, which have several applications in signal/image processing, computer vision and machine learning. Designing computationally efficient and fast-convergence algorithms is a subject of great interest.
This project proposed an innovative methodology to cut back the problem dimension of CAOL and speed up its computation. The team’s method shows quicker convergence than the CAOL acceleration method using majorizers. When applying the learned kernels to sparse-view CT reconstruction, their proposed method provides higher image quality than the original method.
In September 2016, GE Healthcare’s MAGnetic resonance image Compilation (MAGiC) software was approved by the Food and Drug Administration and introduced into the market. The software was the first of its kind to be able to produce eight contrasts in a single acquisition, using lesser time as compared to conventional imaging techniques. The software is also able to provide flexibility to users manipulate MR images retrospectively, eliminating the need for rescans and therefore saving time and costs incurred, leading to a more decisive diagnosis. One feature made possible by the MAGiC software is an acquisition technique which allows the modification of image contrast after scan completion, which is not possible in the conventional MRI.
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